Prista et al: Use of SARIMA models to assess data-poor fisheries 
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SARIMA models to already available landings data we 
were able to carry out a first baseline evaluation of one 
such fishery, using limited funds and minimal time. 
Our study provides a first example of how SARIMA 
models can be used to monitor data-poor fisheries. In 
the case of meagre, the data displayed no trend and 
the 95% SARIMA prediction intervals fully encom- 
passed all monthly landings, thus indicating a stable 
“in-control” fishery. Note that by stating this, at no 
point do we suggest that the meagre fishery is sustain- 
able long-term because landings do not necessarily 
reflect stock abundance and our study was limited in 
time. We suggest only that, since no motive for alarm 
exists in landings data, and because funds, personnel, 
and expertise are limited at the national level, atten- 
tion should be allocated to fisheries that, contrary to 
the meagre, display decreasing trends or out-of-control 
situations. Similar types of pragmatic reasoning are 
generally of great help to fisheries managers handling 
multiple data-poor fishery scenarios because they help 
them prioritize management actions for the subset of 
“problematic” resources in a statistically sound way 
(Scandol, 2003, 2005). 
Underlying the usefulness of SARIMA models in 
monitoring the meagre fishery and other data-poor 
fisheries is the use of prediction intervals as refer- 
ence points to signal alarming trends or sudden level 
shifts in the fisheries process (Caddy, 1999; Scandol, 
2003; Mesnil and Petitgas, 2009). SARIMA Pis have 
been previously reported in the literature (Table 1), 
but their use in monitoring was not explored or formal- 
ized. These intervals are currently the focus of much 
statistical research on how to deal with their tendency 
toward “over-optimism,” i.e., the fact that nominal 95% 
prediction intervals generally contain less than 95% of 
future observations (Chatfield, 1993). Fortunately, from 
a fisheries conservation perspective such over-optimism 
does not constitute a major problem because narrower 
Pis will be more sensitive to changes in the fisheries 
process. 
Statistical process control (SPC) monitoring of uni- 
variate fisheries indicators has become the focus of in- 
creased research attention (Scandol, 2003, 2005; Mesnil 
and Petitgas, 2009; Petitgas, 2009; ICES 1 ). The use of 
SARIMA Pis is similar to that of SPC control-charts, 
which makes them interesting candidates for the simul- 
taneous monitoring of multiple fisheries and fisheries 
indicators (Caddy, 1999; Scandol, 2005; Petitgas, 2009). 
For such cases, SARIMA Pis offer the advantage of be- 
ing model-based and do not require extensive historical 
reference data. They are also free from the assumption 
of statistical independence that frequently troubles the 
estimation of SPC detection limits (Mesnil and Petitgas, 
2009). The simulation framework proposed by Scandol 
(2003, 2005) for SPC charts provides a means whereby 
SARIMA Pis can be calibrated toward specific detec- 
tion rates and management goals. Such calibration 
was beyond the objectives our study but constitutes an 
interesting research route for those in charge of more 
holistic fisheries management. 
SARIMA models in assessments of data-poor fisheries 
Formal stock assessment has traditionally been consid- 
ered as the starting point of any fisheries assessment 
(Mahon, 1997; Berkes et al., 2001). Such an approach 
is highly desirable but will not be implemented easily, 
nor quickly, in the many existing data-poor fisheries 
( Vasconcellos and Cochrane, 2005). In fact, NRC (1998) 
estimated that 16% of U.S. stocks are not subjected to 
assessment; and the European Environmental Agency 
(EE A, 2005) estimated that, depending on the region 
considered, 20-90% of commercial stocks exploited in the 
Northeast Atlantic and Mediterranean are not routinely 
assessed. These figures are much worse in developing 
countries and when discard and bycatch species are 
included in the estimates (Vasconcellos and Cochrane, 
2005). Addressing such situations requires increased 
focus on alternative stock indicators and assessment 
methods that can be used to monitor more fisheries by 
using available (or easily obtainable) data, funds, and 
human resources (e.g., Caddy, 1999; Scandol, 2005; 
Mesnil and Petitgas, 2009; OSPAR, 2010; ICES 1 ). Uni- 
variate time series models fitted to landings data may 
be, for some time longer, the best possible approach to 
extend assessment and management coverage to many 
of these unassessed resources. 
SARIMA modeling and process-control schemes do 
not constitute alternatives to analytical stock assess- 
ment models. Rather, whenever possible, they should 
be seen as statistical tools to support expert judgment, 
funding allocation, and management decisions in the 
most data-limited and assessment-limited settings 
(Scandol, 2003; 2005). SARIMA modeling and model- 
based monitoring have a range of characteristics that 
make them worthy of future exploration in data-poor 
contexts. Among these are their appropriateness to nu- 
merous resources and variables, their strong statistical 
background and ecological plausibility, their good fore- 
casting performance and easy-to-estimate detection lim- 
its, and their applicability to both long and short time 
series. Furthermore, SARIMA models can also be used 
to model the nonspecific groupings that dominate many 
landings data sets, or can be upgraded if multivariate 
data become available (Stergiou et al., 1997; Vascon- 
cellos and Cochrane, 2005). Finally, the availability of 
SARIMA models in open-source software packages and 
their routine use in sectors other than fisheries (e.g., 
sales, economics, engineering) (Brockwell and Davis, 
2002; Box et al., 2008) may be decisive advantages in 
budget-limited and expertise-limited countries. 
Acknowledgments 
Funding for this work was provided by a “Fundagao para 
a Ciencia e a Tecnologia” (FCT) grant BD/12550/2003 to 
N. Prista and by research project CORV (DGPA-Mare: 
FEDER— 22-05— 01-FDR— 00036). We thank Direcgao 
Geral das Pescas e Aquicultura (DGPA) for providing 
the meagre data set. We thank D. S. Stoffer and D. R. 
